A while ago, I came across a hugely insightful video essay titled Why Chinese Words Actually Make Perfect Sense. It broke down the logic behind Chinese vocabulary with such clarity and wit that it completely shifted how I see everyday words.
In the video, the word tuīqiāo (推敲) is classified as Type 4: Parallel structure, where the two characters share closely related meanings. But what makes this word truly fascinating goes far beyond its morphology—the story behind it gives it a layer of meaning steeped in Eastern sensibility and deliberation.
The Tang-dynasty poet Jia Dao was composing a line: "Birds rest by the pond-side tree, / A monk pushes the moonlit gate." Yet he couldn't decide whether to use push (推) or knock (敲). As he walked along, he kept miming both actions, turning them over in his mind, until he inadvertently wandered into the retinue of Han Yu, a high official and literary giant. Instead of punishing him, Han Yu, upon learning of Jia Dao's dilemma, offered a decisive judgment: in the silent stillness of a moonlit courtyard, a single knock would not only heighten the sense of deep-night tranquility but also strike a more courteous and appropriate note—after all, a guest should knock before entering.
From that day on, tuīqiāo transcended its literal meaning of "push and knock" and evolved into a dedicated principle of scholarly and expressive discipline: the art of weighing words, refining phrasing, and reworking logic until the most fitting, most graceful expression emerges.
This underlying logic of tuīqiāo reminded me of a classic concept in computer science: Edit Distance. And that unexpected crossover is what led me to develop a practical, AI‑assisted language‑learning method tailored for ordinary people.
What is Edit Distance?
Edit distance is an algorithm that quantifies the difference between two strings of text. It measures the minimum number of single‑character edits (substitution, deletion, insertion, or transposition) required to transform string A into string B.
A classic example: changing kitten into sitting takes just three operations, so their edit distance is 3. At its core, this algorithm addresses physical‑level text corrections—commonly used in spell‑checking, plagiarism detection, and sentence alignment. It fixes form, not substance.
Machine edit distance corrects surface errors in glyphs, word order, and formatting. Human tuīqiāo, by contrast, optimizes deep‑layer qualities: semantic fit, emotional tone, stylistic grace, and contextual appropriateness. The former is a "standard‑answer correction"; the latter is a "human‑texture iteration."
A New Mindset for Language Learning
Good expression is never an on‑the‑spot gift—it is the predictable result of advance polishing and repeated iteration.
This idea came from an observation: stand‑up comedians, whose seemingly effortless improvisation is actually fragmented inspiration honed into logically tight, fluidly delivered scripts. What looks like spontaneity is always preparation in disguise.
The same is true for everyday speech and foreign‑language communication. When ordinary people stumble, freeze, or sound awkward, the core problem is rarely a lack of talent—it's that we are used to improvising on the spot and have never prepared our expressions in advance.
Based on this, I've developed a fresh approach—let's call it the Prepared‑Expression Iteration Method. It's a sustainable, long‑term language practice algorithm that leverages AI tools to boost bilingual expression at low cost and high efficiency.
The logic is simple: there is nothing new under the sun. Every situation, emotion, experience, or opinion we might want to express in daily life can be pre‑crafted, iterated, and stored for reuse.
I keep a dedicated Expression Practice Booklet—a core repository for daily accumulation and iterative refinement. The entry threshold is extremely low, so anyone can stick with it:
Anything you see, hear, or feel in life can be jotted down anytime: a scene you pass, a small event, a sudden emotion, a beautiful view, an annoyance, an amusing moment—no matter how trivial.
The format is completely flexible: snap a photo and describe it in your target language; if your level is still basic, start by outlining the thought in your native language and translate later. Don't aim for perfection from the start. The sole goal is to turn vague feelings into concrete written words.
The output at this stage will inevitably be full of errors—awkward wording, garbled syntax, and clumsy structures. But that is exactly the starting point for iteration.
Three Parallel Tracks for Refinement
You can mix and match these three paths according to your needs:
AI‑assisted parsing and optimization – Use large language models to batch‑fix grammatical mistakes, smooth out stiff expressions, and nudge them toward native‑like fluency, quickly producing an upgraded baseline version.
Native‑speaker fine‑tuning – For colloquial, idiomatic needs, consult native speakers to replace unnatural phrasing with more local, organic alternatives.
Controlled self‑iteration – Inspired by the edit‑distance logic, set a small‑change rule for yourself: each round, only modify, replace, or move up to 3 words or phrases. Avoid wholesale rewrites that make it hard to track progress. Iterate step by step to steadily enhance expressiveness.
A Crucial Reminder
Language communication never requires 100% perfection, and there is no single "correct" answer for any expression.
Our iterations and refinements are not aimed at producing flawless academic prose, but at making our speech and writing smoother, more natural, and more precise. Every edit, every round of polishing, and every quality piece of text we eventually save becomes part of our own pre‑stocked expression library.
Don't rush. This is a game of small, long‑term gains. Day after day, as we record, weigh, and iterate, we will gradually break free from the anxiety of freezing up or sounding awkward. Every future utterance will be backed by preparation, confidence, and substance.
Fluency, after all, is simply the result of countless rounds of advance tuīqiāo.
A Final Note
I've decided to call this concept "pre‑made language" (预制外语). Our advance preparation is essentially writing a set of ready‑made "conditioned‑reflex scripts" for our brain. It's like learning to swim: you use buoyancy and motion (intuition and habit) to counter gravity (forgetting).
Moving forward, I plan to implement this idea as a feature in Atobe, tentatively named the Expression Practice Booklet (ExprBook) , to realize the "AI scoring" item I listed in my March roadmap. I've been mulling this over for a long time, and now I finally have a solid, actionable plan—though it won't exactly be "scoring" in the conventional sense. I'll also add a quantifiable scoring algorithm as a supplementary reference, hoping to refine a comprehensive and effective learning strategy.